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Euclid preparation
V. Guglielmo, R. Saglia, F. Castander, A. Galametz, S. Paltani, R. Bender, M. Bolzonella, P. Capak, O. Ilbert, D. Masters, et al.
To cite this version:
V. Guglielmo, R. Saglia, F. Castander, A. Galametz, S. Paltani, et al.. Euclid preparation: VIII.
The Complete Calibration of the Colour–Redshift Relation survey: VLT/KMOS observations and data release. Astronomy and Astrophysics - A&A, EDP Sciences, 2020, 642, pp.A192. �10.1051/0004- 6361/202038334�. �hal-03026941�
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V. Guglielmo et al. 2020
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Astrophysics
Euclid preparation
VIII. The Complete Calibration of the Colour–Redshift Relation survey:
VLT/KMOS observations and data release
?Euclid Collaboration: V. Guglielmo
1, R. Saglia
1,2, F. J. Castander
3,4, A. Galametz
5, S. Paltani
5, R. Bender
1,2, M. Bolzonella
6, P. Capak
7,8, O. Ilbert
9, D. C. Masters
10, D. Stern
11, S. Andreon
12, N. Auricchio
6,
A. Balaguera-Antolínez
13,14, M. Baldi
6,15,16, S. Bardelli
6, A. Biviano
17,18, C. Bodendorf
1, D. Bonino
19, E. Bozzo
5, E. Branchini
20, S. Brau-Nogue
21, M. Brescia
22, C. Burigana
23,24,25, R. A. Cabanac
21, S. Camera
19,26,27,
V. Capobianco
19, A. Cappi
6,28, C. Carbone
29, J. Carretero
30, C. S. Carvalho
31, R. Casas
3,4, S. Casas
32, M. Castellano
33, G. Castignani
34, S. Cavuoti
22,35,36, A. Cimatti
15,37, R. Cledassou
38, C. Colodro-Conde
14,
G. Congedo
39, C. J. Conselice
40, L. Conversi
41,42, Y. Copin
43, L. Corcione
19, A. Costille
9, J. Coupon
5, H. M. Courtois
44, M. Cropper
45, A. Da Silva
46,47, S. de la Torre
9, D. Di Ferdinando
16, F. Dubath
5, C. A. J. Duncan
48,
X. Dupac
42, S. Dusini
49, M. Fabricius
1, S. Farrens
32, P. G. Ferreira
48, S. Fotopoulou
50, M. Frailis
18, E. Franceschi
6, M. Fumana
29, S. Galeotta
18, B. Garilli
29, B. Gillis
39, C. Giocoli
6,15,16, G. Gozaliasl
51,52, J. Graciá-Carpio
1, F. Grupp
1, L. Guzzo
12,53,54, H. Hildebrandt
55, H. Hoekstra
56, F. Hormuth
57, H. Israel
2, K. Jahnke
58, E. Keihanen
52,
S. Kermiche
59, M. Kilbinger
32,60, C. C. Kirkpatrick
52, T. Kitching
45, B. Kubik
61, M. Kunz
62, H. Kurki-Suonio
52, R. Laureijs
63, S. Ligori
19, P. B. Lilje
64, I. Lloro
65, D. Maino
29,53,54, E. Maiorano
6, C. Maraston
66, O. Marggraf
67, N. Martinet
9, F. Marulli
6,15,16, R. Massey
68, S. Maurogordato
69, E. Medinaceli
6, S. Mei
70,71, M. Meneghetti
72,
R. Benton Metcalf
15,73, G. Meylan
34, M. Moresco
6,15, L. Moscardini
6,15,16, E. Munari
18, R. Nakajima
67, C. Neissner
30, S. Niemi
45, A. A. Nucita
74,75, C. Padilla
30, F. Pasian
18, L. Patrizii
16, A. Pocino
3,4, M. Poncet
38, L. Pozzetti
6, F. Raison
1, A. Renzi
49,76, J. Rhodes
11, G. Riccio
22, E. Romelli
18, M. Roncarelli
15,72, E. Rossetti
15, A. G. Sánchez
1, D. Sapone
77, P. Schneider
67, V. Scottez
60, A. Secroun
59, S. Serrano
3,4, C. Sirignano
49,76, G. Sirri
16,
F. Sureau
32, P. Tallada-Crespí
78, D. Tavagnacco
18, A. N. Taylor
39, M. Tenti
16, I. Tereno
31,46, R. Toledo-Moreo
79, F. Torradeflot
78, A. Tramacere
5, L. Valenziano
16,72, T. Vassallo
2, Y. Wang
10, N. Welikala
39, M. Wetzstein
1,
L. Whittaker
80,81, A. Zacchei
18, G. Zamorani
6, J. Zoubian
59, and E. Zucca
6(Affiliations can be found after the references) Received 4 May 2020/Accepted 16 June 2020
ABSTRACT
The Complete Calibration of the Colour–Redshift Relation survey (C3R2) is a spectroscopic effort involving ESO and Keck facilities designed specifically to empirically calibrate the galaxy colour–redshift relation –P(z|C) to theEucliddepth (iAB =24.5) and is intimately linked to the success of upcoming Stage IV dark energy missions based on weak lensing cosmology. The aim is to build a spectroscopic calibration sample that is as representative as possible of the galaxies of theEuclidweak lensing sample. In order to minimise the number of spectroscopic observations necessary to fill the gaps in current knowledge of theP(z|C), self-organising map (SOM) representations of the galaxy colour space have been constructed. Here we present the first results of an ESO@VLT Large Programme approved in the context of C3R2, which makes use of the two VLT optical and near-infrared multi-object spectrographs, FORS2 and KMOS. This data release paper focuses on high-quality spectroscopic redshifts of high-redshift galaxies observed with the KMOS spectrograph in the near-infraredH- andK-bands. A total of 424 highly-reliable redshifts are measured in the 1.3≤z≤2.5 range, with total success rates of 60.7% in theH-band and 32.8% in theK-band. The newly determined redshifts fill 55% of high (mainly regions with no spectroscopic measurements) and 35% of lower (regions with low-resolution/low-quality spectroscopic measurements) priority empty SOM grid cells. We measured Hαfluxes in a 1.002 radius aperture from the spectra of the spectroscopically confirmed galaxies and converted them into star formation rates. In addition, we performed an SED fitting analysis on the same sample in order to derive stellar masses,E(B−V), total magnitudes, and SFRs. We combine the results obtained from the spectra with those derived via SED fitting, and we show that the spectroscopic failures come from either weakly star-forming galaxies (atz<1.7, i.e. in theH-band) or low S/N spectra (in the K-band) ofz>2 galaxies.
Key words. catalogs – surveys – cosmology: observations – galaxies: distances and redshifts
? Full Table 5 is only available at the CDS via anonymous ftp tocdsarc.u-strasbg.fr(130.79.128.5) or via http://cdsarc.u-strasbg.fr/viz-bin/cat/J/A+A/642/A192
Open Access article,published by EDP Sciences, under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
A192, page 1 of19
1. Introduction
The existence of a direct connection between cosmic shear and the presence of gravitational fields created by the distri- bution of matter along the line of sight motivated the develop- ment of a number of weak lensing cosmological surveys. These are both space based, such asEuclid(Laureijs et al. 2011) and WFIRST (Spergel et al. 2015), and ground based, such as the ongoing Kilo-Degree Survey (KiDS,de Jong et al. 2013), Dark Energy Survey (DES,Dark Energy Survey Collaboration 2016), Hyper Suprime-Cam Subaru Strategic Programme (HSC SSP, Aihara et al. 2018), and the future Vera C. RubinObservatory survey (LSST, LSST Science Collaboration 2009). The main advantage of space missions with respect to ground-based ones is the absence of atmospheric turbulence, which leads to images with smaller and more stable point-spread functions (PSFs), allowing cosmological analyses at higher redshifts. Besides tur- bulence, space is key for near-infrared observations, thanks to the lower background, which makes it possible to reach higher redshift than the ground-based surveys.
The aims of the aforementioned projects are to determine galaxy shape distortions, make use of weak lensing principles to measure the geometry of the Universe, and trace the evo- lution of large-scale structure (LSS) to shed light on the com- plex relation between galaxies and the dark components of the Universe. In this respect, the outcome of these ambitious pro- grammes heavily depends on the precise determination of the true ensemble redshift distribution, orN(z), and thus an accurate reconstruction of the 3D distribution of galaxies. To the lowest order, weak lensing is primarily sensitive to the mean redshift and the width of the redshift distribution in tomographic bins (Amara & Réfrégier 2007).
Moreover, the sensitivity of weak lensing tomography to the dark energy equation of state cannot disregard the abil- ity to measure the growth of structure by dividing the source samples by redshift. The difficulty of finding optimal tomo- graphic redshift bins for cosmic shear analysis has been studied in recent works, and solutions based on dimensionality reduction approach through self-organising maps (SOM, Kohonen 2001) have been explored (Kitching et al. 2019).
In the case ofEuclid, this translates into stringent require- ments on the knowledge of the redshift distribution of sources evaluated in terms of (1) the precision of individual redshifts, which must beσz<0.05(1+z), and (2) the mean redshifthziof each tomographic bin, which must be constrained at the level of
∆hzi ≤0.002(1+hzi).
The Euclid satellite, scheduled for launch in 2022, will observe galaxies out to at leastz=2 over 15 000 deg2by means of two instruments: VIS, an optical imager that will reach an AB magnitude depth of 24.5 with a single broadr+i+zfilter, and NISP, a combined near-infrared imager (inY,JandH) and slit- less spectrograph. The estimated number of weak lensing source galaxies that will be imaged fromEuclid makes their system- atic spectroscopic follow-up unfeasible; this mission is thus crit- ically dependent upon the determination of accurate photometric redshifts (zphot). However, the accuracy of current photometric redshifts based on multi-band optical surveys is to the order of σz/(1+z) = 0.03−0.06,and the fraction of catastrophic out- liers – defined as objects whosezphotdiffers from their spectro- scopic redshift (zspec) by more than 0.15(1+z) is to the order of a few tens of percent (Ma et al. 2006;Hildebrandt et al. 2010).
While small changes in zphot precision per source have a rela- tively small impact on cosmological parameter estimates, small
systematic errors inzphotcan dominate all other uncertainties for these experiments.
In this work, we present the results of all the redshift mea- surements onz>1 galaxies performed during five semesters in the context of an ESO Large Programme at the Very Large Tele- scope (VLT, the detailed presentation can be found in Sect.2), using the near infrared KMOS spectrograph. The campaign con- ducted with FORS2 on the lower redshift targets will be pre- sented in a companion paper (Castander et al., in prep.). The paper is organised as follows: Sect.2 presents the concept and the characteristics of the C3R2 survey; in Sect.3, we present the survey strategy; in Sect.4we describe the observations and data reduction; in Sect.5, we discuss the redshift determination and the attribution of a flagging scheme consistent over the whole C3R2 survey; in Sect.6, we present the results of the redshift assignment in terms of success rate and SOM cell coverage; in Sect. 7, we determine and discuss the galaxy physical proper- ties in terms of Hαfluxes and stellar masses and investigate their location in the star formation rate stellar mass (SFR–M?) plane;
finally, we present our conclusions in Sect.9.
Throughout the paper, we assume H0 = 70 km s−1Mpc−1, Ωm = 0.3,ΩΛ =0.7. We adopt aChabrier(2003) initial mass function (IMF) in the mass range 0.1−100M.
2. Mapping the colour–redshift relation with spectroscopy
In order to overcome the limitations of current techniques used to estimaten(z), a complete calibration set of spectroscopic data is required. This spectroscopic calibration sample should be rep- resentative of the entire range of galaxy types and redshifts that are going to be exploited by a given weak lensing survey.
2.1. Dimensionality reduction approach to P(z|C) calibration In order to shed light on our current knowledge of the galaxy population for weak lensing measurements, and in particular for Euclid,Masters et al.(2015, hereafter M15) made use of a SOM to map the high-dimensional galaxy colour space onto a 2D plane. We used the SOM to group galaxies according to the sim- ilarity of their colours (i.e. of their spectral energy distributions;
SEDs) in order to unveil which regions of the galaxy colour space (represented by cells in the plane) are not represented in currently available spectroscopic surveys. This grouping strategy allows us, in turn, to minimise the number of additional spectro- scopic redshifts necessary to build a complete and representative calibration sample. The underlying assumption of this method- ology is that, for a dense enough SOM and a sufficiently high- dimensional colour space, there is a unique and non-degenerate relation between the position occupied by a galaxy in a multi- colour space and its redshift – P(z|C). Similar dimensionality reduction approaches in the context of weak lensing cosmolog- ical surveys have been used in recent works, using, for exam- ple, absolute magnitudes instead of colours in order to calibrate photometric redshifts (Wright et al. 2020). The authors stress the importance of using magnitudes as a reference to an absolute flux scale in order to calibrate then(z) forEuclid. Starting from a photometric sample of galaxies selected using theEuclidmag- nitude limit and grouped using theEuclidcolours and the cor- responding spectroscopic sub-samples available in the Cosmo- logical Evolution Survey (COSMOS,Scoville et al. 2007) field, M15 estimated that a total collection of∼10−15 K spectra would be necessary in order to fill the galaxy colour space and cover
the whole set of parameters characterising the galaxy population that will be observed byEuclid. About half of them are already available from various spectroscopic surveys in the literature, whereas approximately 5000 new redshifts should be observed in order to calibrate the current photometric redshift techniques and meet theEuclidrequirements. Galaxies in these unexplored regions of colour space are generally fainter thaniAB ∼23 and lie at intermediate redshift, 0.2 < z < 2.0; they correspond to a population of faint, blue galaxies at intermediate redshift, which have not been targeted because they are near the mag- nitude limit of previous surveys. However, their abundance and unique colours make them an important part of the galaxy pop- ulation and crucial sources for weak lensing cosmology. Based on their spectral energy distributions, we expect the objects tar- geted to be mostly low-metallicity galaxies with strong emission lines. A minor number of cells contain faint red galaxies that are either passively evolving or dust obscured, but these consti- tute only 10−20% of the unexplored sample. Hence, M15 col- lected a large number of existing spectroscopic measurements in the COSMOS field (Capak et al. 2007; Scoville et al. 2007;
Lilly et al. 2007) to identify the type (and number) of sources that require spectroscopic follow-up in order to accurately map the full colour-redshift relation of galaxies. The work has since then been extended to four additional fields: the VIMOS VLT Deep Survey (VVDS) field, the Subaru/XMM-NewtonDeep Sur- vey (SXDF) field, the Extended Groth Strip field (EGS, within the All-Wavelength EGS International Survey, AEGIS), and the ExtendedChandraDeep Field-South (E-CDFS) field.
2.2. C3R2 overview
The Complete Calibration of the Color–Redshift Relation (C3R2;Masters et al. 2017; M17 hereafter) survey was designed to perform a systematic spectroscopic effort by means of two observing campaigns involving two telescope facilities. Part of the spectroscopic follow-up is conducted with the Keck tele- scopes using a combination of the DEIMOS, LRIS, and MOS- FIRE instruments, with time allocated from all Keck partners (M17). The second part is overseen by the ESO Very Large Tele- scope (VLT) and its UT1 instruments FORS2 and KMOS.
M17 presented the results of the first five nights of obser- vations using the Keck facilities during the 2016A semester, leading to the release of 1283 high-confidence redshifts (Data Release 1). A further 3171 new high-quality spectroscopic red- shifts were obtained during 2016B and 2017A semesters and are released in Masters et al. (2019, M19, Data Release 2). A third C3R2@Keck data release is in preparation (Stanford et al., in prep.).
2.3. C3R2@VLT
In order to build a large sample of spectroscopic redshifts for the calibration of the photometric redshifts of upcoming cosmologi- cal surveys we obtained a 200 h large programme (199.A-0732;
PI F. J. Castander) in service mode over four semesters (Period P99: 1st April 2017 – P102: 31st March 2019+carryover). The large programme allocated 112 h to FORS2, a multi-object opti- cal slit spectrograph and 88.8 h to KMOS, an integral field unit (IFU) spectrograph covering the near-infrared wavelength regime. KMOS observations were automatically carried over P103 to complete a few P102 pointings in the SXDF field. The VLT campaign targets the same extragalactic fields observed with the Keck programme with the exception of EGS, which is not accessible from the southern hemisphere.
3. Target selection and KMOS IFU settings 3.1. Observed fields
In order to reduce the impact of sample variance on the calibra- tion of photometric redshifts, the spectroscopic follow-up obser- vations are conducted in a number of extragalactic calibrations and deep fields planned for the Euclid mission. However, we expect these commonly observed fields to also be the calibration fields of other upcoming surveys such as LSST and WFIRST;
this spectroscopic follow-up effort will therefore be beneficial for the wide field survey community at large.
The major driving criterion in the choice of such fields is the possibility of collecting a homogeneous and well-calibrated pho- tometric sample of galaxies observed in eight filters (ugrizY JH, seven colours) from the optical to the near-infrared domain down to the Euclid limiting magnitude but with five times higher signal-to-noise ratio. A combination of the Canada-France- Hawaii Telescope Legacy Survey (CFHTLS) deep fields in the ugriz optical magnitude and the VISTA or CFHT-WIRCAM Deep Survey (WIRDS) in the Y HK near-infrared bands was found to meet these requirements. The finally targeted fields are COSMOS (from which the SOM was derived; RA=10h0m, Dec=2◦120), the VIMOS-VLT Deep Survey field centred at RA=2h (VVDS-02h, VVDS hereafter; Le Fèvre et al. 2005;
RA=2h26mDec=−4◦300), the Subaru/XMM-NewtonDeep Sur- vey field (SXDF;Furusawa et al. 2008; RA=2h18mDec=−5◦), and the Extended Chandra Deep Field-South Survey field (ECDFS;Lehmer et al. 2005; four fields centred at the follow- ing coordinates: Field 1, RA=3h33m5s.61 Dec=−27◦4108.0084;
Field 2, RA=3h31m51s.43 Dec = −27◦41038.0080; Field 3, RA
=3h31m49s.94 Dec=−27◦57014.0056; Field 4, RA=3h33m2s.93 Dec = −27◦57016.0008). The Keck part of C3R2 additionally targets the Extended Groth Strip field (EGS; RA=14h19m Dec=52◦410), inaccessible to VLT facilities. We note that the SXDF and E-CDFS fields currently lack uniform photome- try in the full suite of the aforementioned optical and near- infrared filters at the required depth, but as they provide a con- siderable number of spectroscopic redshifts, they were included after applying a rough colour correction to convert into the CFHTLS+VISTA/WIRDS-like system (see M17).
3.2. Prioritisation scheme and target selection
C3R2 prioritises targets in regions of the SOM that lack spec- troscopic redshifts. High-priority targets have colours that are frequent (i.e. fall in cells with high occupation) and are therefore extremely valuable in calibrating the redshift-to-colour relation.
The C3R2 prioritisation scheme (extensively described in M19) therefore gives higher weights to sources with common colours in still uncharted cells. As observations are obtained and spec- troscopic redshifts determined, the target catalogue and priority flags are updated.
Spectroscopic redshift measurements are based on the iden- tification of emission lines in the observed galaxy spectra, with higher priority given to the detection of the often prominent Hα line (λ6564.61 Å1). The grisms selected for the KMOS
1 In order to operate at near-infrared wavelengths, the entire working parts of the instrument are cooled to below −130◦C with the detec- tor cooled even further to below−200◦C. To achieve this, the entire instrument is contained in a vacuum within a cryostat to prevent icing and extra heat load on the fragile components. Therefore, the wave- length values should be considered in rest-frame vacuum units (e.g.
Hαrestframe=6564.61 Å).
observations areH(1.456−1.846µm) andK(1.934−2.460µm);
we thus target galaxies with a photometric redshift that positions the Hα line within the observed wavelength range but avoids its contamination by atmospheric absorption windows as well as OH night-sky emission lines, as shown in Fig.1.
We selected high-redshift star-forming galaxy candidates with 1.3 < zphot < 1.7 and 2.0 < zphot < 2.5 to be observed with theHandKgrisms, respectively, and divide them into two classes based on the prioritisation scheme defined in M19:
– H-band, priority 1: 1.3 ≤ zphot ≤ 1.7,itot ≤ 24.5, and the priority flag computed in M19 (PF)≥5002;
– H-band, priority 2: 1.3≤zphot ≤1.7,itot ≤24.5, and 200≤ PF<500;
– K-band, priority 1: 2.0 ≤zphot ≤2.5,itot ≤24.7, andPF ≥ 500;
– K-band, priority 2: 2.0≤zphot≤2.5,itot≤24.7, and 200≤ PF<500.
The H-band PF ≥ 500 corresponds to the top 7.2% of KMOS selection list, PF ≥ 200 corresponds to the top 18%. K-band priority>500 corresponds to the top 16% of the KMOS selection list, priority>200 corresponds to the top 33%.
A fraction of the COSMOS, SXDF, and E-CDFS fields have been extensively observed in the past with KMOS as part of the KMOS3D programme, one of the KMOS Guarantee Time Observations programmes (Wilkinson et al. 2015) using theY J, H, andKgratings. We removed all sources already observed by the KMOS3D team from the present target selection. Their spec- troscopic redshifts (of exquisite precision) are available publicly (Wisnioski et al. 2019) and are going to be used for the calibra- tion of theEuclidphotometric redshifts (KMOS3D3).
4. Observations and data reduction
In this section, we describe the acquisition and reduction of the data.
4.1. Observation design
KMOS is a multiplexed near-infrared integral field system (IFS) with 24 deployable image slicers (commonly referred to as
“arms”), surveying a 7.02 diameter patrol field area. Each arm has a field of view (FoV) of 2.008×2.008 (14×14 pixel IFS units) and a spatial resolution of 0.002/spaxel. The IFS units connect to three cryogenic grating spectrometers with 2k×2k Hawaii- 2RG HgCdTe detectors. As previously mentioned, among the five available KMOS gratings (IZ,Y J,H,K,HK), our obser- vations make use of the H- and K-bands (plus tentative Y J), characterised by a typical spectral resolution of about 3500.
The observations were prepared with the KMOS ARM Alloca- tor (KARMA;Wegner & Muschielok 2008) software, and sub- mitted through the Phase 2 Proposal Preparation (P2PP) tool.
Hereafter an individual KARMA setup (made of 24 arm alloca- tions) is referred to as a “pointing”. Each pointing was observed for a total of 3600 s split into single exposures of 300 s each, using an O-S-O-O-S-O pattern (i.e. a “sky” exposure is observed every two “object” exposures). The sky exposures were offset with respect to targets to the closest position uncontaminated by sources. Additional sub-pixel/pixel dithering shifts were also applied at every exposure to minimise the impact of pixel-to- pixel variation and bad pixels in the final science data cube. One
2 ThePFparameter computed in M19 ranges from 0 up to 3750; 89%
of the SOM cells havePF≤500.
3 http://www.mpe.mpg.de/ir/KMOS3D/data
Fig. 1. Telluric absorption curve (black curve) in wavelength range covered by the KMOSH- andK-band gratings (red horizontal lines);
the light grey spectrum in the bottom part of the panel represents the emission lines produced by the OH radical in the atmosphere between 0.61µm and 2.62µm. The red labels on the top horizontal axis indicate the redshift (1.4<z<2.6) of a galaxy whose Hαemission line falls at the wavelength indicated by the position of the vertical red dashed lines.
of the 24 KMOS IFUs was allocated to a star (with an observed magnitude of 15.0<H<16.5) during the science observations (with the exception of 7/36 pointings). The star allows us to track variations in the PSF and photometric conditions between the frames; the star is therefore referred to as the PSF star.
The standard requirements of the KARMA software for preparing a KMOS pointing are, firstly, the presence of a suf- ficient number of acquisition stars (with observed magnitudes 13.5 < H < 17) within the patrol field of a given KMOS pointing and preferentially and equally distributed among the 24 arms and three spectrometers/detectors (these stars are used to align KMOS). The second requirement is the absence of bright stars (which would create persistency) superposed with the path of the KMOS arms on the field of view. The final require- ment is the presence of at least one bright guide star (with an observed magnitude 9 < R < 12) in the vicinity of the point- ing to maintain telescope tracking. All the aforementioned stellar sources must have low proper motion. Specifically, we required
|µRA| and |µDec|<20 mas yr−1.
The observations cover four distinct fields whose observabil- ity spreads adequately throughout the year. The number of hours allocated per semester and per field is reported in Table1. The corresponding number of pointings are indicated in parentheses, split between theH- andK-bands, with a slight preference of H-band overK-band to maximise the redshift measurement suc- cess rate. A detailed list of the pointings observed in P99–P103 is reported in Table2. Each observing block (OB) is composed of two pointings of 1 h on sky, which provides about 40 minutes on source. These pointings can either be observed during the same night or on different nights. In the latter case, the obser- vations are reduced separately and then combined. Only during the last awarded period (P102) was the on-source time for K- band pointings doubled in order to increase the detectability of the targeted galaxies. The data-reduction procedure, described in the next section, is applied to the single science and sky frames separately, and the frames are combined at the end of the reduc- tion, after the whole pointing (two OBs) has been observed.
4.2. Data reduction
The data were reduced with the Software Package for Astronom- ical Reduction with KMOS (SPARK;Davies et al. 2013) using
Table 1.Awarded time (in h) for KMOS observations.
Field P99 P100 P101 P102 Total
COSMOS 7.6 10.8 0 10.8 29.2
(2H+1.5Y J(?)) (3H+2K) (5H) (10H+2K)
ECDFS 0 0 2.2 0 2.2
(1H) (1H)
SXDF 0 8.7 5.4 10.8 24.9
(2H+2K) (1H+1K) (3H(??)+1K) (6H+4K)
VVDS 6.5 10.8 6.5 8.7 32.5
(2H+1K) (3H+2K) (2H+1K) (2H+2K) (9H+6K)
Total 14.1 30.3 14.1 30.3 88.8
Notes.The table lists: number of hours, in parenthesis, the number of the observed pointings is indicated, together with the selected filter, for example, 3H+2Kmeans that three pointings have been observed in theH-band and two pointings have been observed in theK-band.(?)We had initially planned to target sources with 1.8<zphot <2.0, for which the Oiidoublet is in theY J-grating. The detection of Oiiis challenging in high-redshift galaxies, and our first observations in P99 had a low success rate. We therefore decided to start in P100 to exclusively concentrate on the detection of Hαin theH- andK-gratings.(??)The observation of the last threeH-band pointings in the SXDF field (see Table2for details) was carried over P103.
Table 2.Observed pointings.
Pointing ID RAcen Deccen Exp_time Filter UT date Success rate
(deg) (deg) (s) (yyyy.mm.dd) (3≤Q≤4/Q=2/Observed)
P99_COSMOS_HaHP1 149.8900 1.9003 2×1800 H 2017.04.03 14/4/22
P99_COSMOS_HaHP3 150.1672 1.8391 1800 H 2017.04.02 16/4/22
1800 H 2017.04.04
P99_VVDS_HaHP2 36.3758 −4.2529 2×1800 H 2017.12.23 18/2/22
P99_VVDS_HaHP3 36.2548 −4.4108 1800 H 2017.09.06 14/3/22
1800 H 2017.09.14
P99_VVDS_HaKP1 36.2005 −4.0997 1800 K 2017.07.12 5/5/22
1800 K 2017.09.14
P100_COSMOS_HaHP1 150.3757 2.5168 1800 H 2018.03.03 12/1/22
1800 H 2018.03.17
P100_COSMOS_HaHP2 150.3964 2.4168 2×1800 H 2018.03.24 11/0/22
P100_COSMOS_HaHP3 150.3342 2.3114 2×1800 H 2018.04.07 17/2/22
P100_COSMOS_HaKP1 150.3758 2.5113 1800 K 2018.03.17 7/1/22
1800 K 2018.03.24
P100_COSMOS_HaKP2 150.4966 2.5003 1800 K 2018.04.04 9/0/22
1800 K 2018.04.06
P100_SXDF_haHP1 34.6131 −5.3581 2×1800 H 2017.10.01 8/2/22
P100_SXDF_haHP2 34.7924 −4.8665 2×1800 H 2017.12.25 16/0/22
P100_SXDF_haKP1 34.6130 −5.3587 1800 K 2017.10.26 3/1/22
1800 H 2018.07.29
P100_SXDF_haKP2 34.7925 −4.8669 1800 K 2018.07.27 1/0/22
1800 H 2018.09.09
P100_VVDS_haHP1 36.5006 −4.0833 1800 H 2018.01.20 14/3/22
1800 H 2018.01.21
P100_VVDS_haHP2 36.6674 −4.4833 1800 H 2018.07.29 12/5/22
1800 H 2018.08.27
P100_VVDS_haKP1 36.6257 −4.4940 1800 K 2017.11.09 7/3/22
1800 K 2018.01.15
P100_VVDS_haKP2 36.7924 −4.5337 1800 K 2018.09.08 11/0/22
1800 K 2018.09.09
P100_VVDS_haHP3 36.7922 −4.4501 1800 H 2018.10.30 11/0/22
1800 H 2018.10.31
P101_ECDFS_haHP1 53.0840 −27.7418 1800 H 2018.07.03 12/1/22
1800 H 2018.08.30
P101_SXDF_haHP1 34.0842 −5.1167 1800 H 2018.09.03 12/0/22
Table 2.continued.
Pointing ID RAcen Deccen Exp_time Filter UT date Success rate
(deg) (deg) (s) (yyyy.mm.dd) (3≤Q≤4/Q=2/Observed)
1800 H 2018.10.31
P101_SXDF_haKP1 34.3047 −5.3420 2×1800 K 2018.12.09 5/1/22
1800 K 2018.12.11
1800 K 2018.12.14
P101_VVDS_haHP1 36.8047 −4.1669 2×1800 H 2018.09.04 17/0/22
P101_VVDS_haHP2 36.9217 −4.5527 2×1800 H 2018.12.14 15/1/22
P101_VVDS_haKP1 36.7296 −4.4668 2×1800 K 2018.11.12 8/0/22
P102_P100_VVDS_HaKP1(?) 36.6257 −4.4944 1800 K 2018.12.20 15/3/22
1800 K 2018.12.21
P102_P99_VVDS_HaKP1(?) 36.2009 −4.1000 1800 K 2018.12.21 15/0/22
1800 K 2018.12.22
P102_VVDS_HaHP1 36.5424 −4.8001 1800 H 2018.12.22 12/3/22
1800 H 2018.12.24
P102_VVDS_HaHP2 36.3672 −4.2446 2×1800 H 2018.12.24 14/2/22
P102_COSMOS_HaHP1 150.0840 2.2193 1800 H 2019.02.14 8/5/22
1800 H 2019.02.23
P102_COSMOS_HaHP2 150.2464 1.8080 1800 H 2019.02.21 12/0/22
1800 H 2019.02.23
P102_COSMOS_HaHP3 149.7305 2.1500 2×1800 H 2019.02.22 12/0/22
P102_COSMOS_HaHP4 149.8884 2.5663 1800 H 2019.02.27 14/0/22
1800 H 2019.03.12
P102_COSMOS_HaHP5 150.4503 2.0366 2×1800 H 2019.01.19 12/0/22
P102_SXDF_HaKP1 34.6756 −5.2782 1800 K 2019.01.25 1/0/22
1800 K 2019.02.13
1800 K 2019.02.14
1800 K 2019.02.18
P102_SXDF_HaHP1 34.6673 −5.2670 1800 H 2019.02.19 12/3/22
1800 H 2019.07.14
P102_SXDF_HaHP2 34.2004 −5.2056 1800 H 2019.07.17 9/3/22
1800 H 2019.07.18
P102_SXDF_HaHP3 34.6981 −5.0032 1800 H 2019.07.30 10/0/22
Notes. (?)These pointings are replicated configurations of two K-band VVDS pointings with low success rates observed during P99 (P102_P99_VVDS_HaKP1) and P100 (P102_P100_VVDS_HaKP1); the overall configuration is maintained, but new objects have been allo- cated to arms in which a good spectroscopic redshift was derived during the earlier observations (quality flag from three to four, which means that we replaced five to seven galaxies per pointing).
recipes outlined in the SPARK instructional guide4. The reduc- tion first applies a correction for detector effects, including (1) the correction of the readout channel variations via the refer- ence pixels (pixels without photodiodes but with full electron- ics readout), and (2) the correction for the picture-frame effects affecting IFUs at the edges of the detector, using median DARK frames. The reduction then proceeds through the standard cali- bration steps, namely flat fielding, illumination correction, wave- length calibration (the accuracy of the wavelength solution is to the order of 30 km s−1), reduction of the spectrophotometric standards, and finally the data cube reconstruction. After this stage, an additional custom processing was performed on these reconstructed data cubes to further subtract the sky lines. The custom-made sky-line correction routine is an adaptation of the Zurich Atmosphere Purge (ZAP; Soto et al. 2017) approach to the KMOS data. The routine subtracts the closest sky frame to the science frame in the O-S-O-O-S-O sequence and then fur- ther optimises the fitting to the OH sky-line residuals via a ZAP principal-component analysis (Wisnioski et al. 2019). The back-
4 ftp://ftp.eso.org/pub/dfs/pipelines/kmos/
kmos-pipeline-cookbook-0.9.pdf
ground continuum is removed using offset sky frames without attempting to correct for short time scale background variations, and thus some residual continuum levels are still expected. An illumination correction is then applied to flatten out the IFU spatial response. A heliocentric correction is finally performed before the data cubes are combined.
A further set of reduction steps is applied by means of a routine developed by the KMOS GTO team in order to perform the flux calibration and a refined background sub- traction (Wisnioski et al. 2019). The flux calibration procedure can be summarised in three operations: (a) correction for the grism+detector wavelength response using a telluric star; (b) application of the zero point to convert fluxes to units of 10−17W m−2µm−1 (to be further multiplied by 0.1 to obtain erg cm−2s−1Å−1); and (c) fit of the PSF star in the science data with a Moffat function for the monitoring of the flux and esti- mation of the PSF from its average FWHM across the frames, and measured again on the combined data cubes for consistency checks. Individual frames are then median-combined into final cubes using spatial shifts measured from the average centre of the stars within the same pointings (when applicable) or using the information given in the header of each frame. Variations
in flux and seeing among the combined frames are typically 10% and 0.001, respectively. A detailed description of the data reduction for KMOS data cubes can be found inWisnioski et al.
(2019).
5. Redshift assignment
The observational programme performed with KMOS@VLT aims to derive the spectroscopic redshift of 1.3 . zphot
.2.5 galaxies through a single emission line, mainly Hαin the H- andK-band filters.
Each observed spectrum was analysed by two co-authors to independently determine the redshift and the quality flag. The results were then reconciled and discussed by the two people. We developed an interactive routine that we applied to the reduced and combined data cubes for the redshift assignment. There are several steps towards the application of the code:
– when continuum is visible, find the position of the targeted source in the spatial plane of the median image of the data cube, otherwise we use the nominal centre at the pixel with coordinates (x,y)=(9,9);
– create two-dimensional (2D) vertical/horizontal spectra computing the median flux at each wavelength of four lines/columns around the central pixel;
– identify the presence of an emission line either in the vertical and/or in the horizontal 2D spectrum and select a narrower (about 10 pixels) wavelength range to determine the pixels where the emission is detected;
– plot the (x,y) spatial image of the cube at four pixels cor- responding to the wavelengths where the emission has the highest intensity in order to identify both the wavelength (in pixel units) of the peak of the emission and the (x,y) coordi- nates of its centre;
– plot the 1D spectrum of the selected central spaxel and the 1D spectrum obtained by summing the flux in a number of adjacent pixels to increase the signal to noise (the number of pixels varies from a cross of five to a square of nine, depend- ing on the spatial extension of the source);
– perform a Gaussian fit weighted by the noise spectrum on the identified emission line;
– choose the most appropriate-looking value of the emission- line centre, between the position of the mean of the fitted Gaussian and the position of the peak pixel;
– compute the redshift with the formula
zspec=(λpeak/Gaussian−λHα)/λHα, (1)
whereλpeak/Gaussianis the wavelength (inµm) corresponding to the pixel peak or to the centre of the fitted Gaussian, and λHαis the Hαvacuum wavelength expressed inµm.
5.1. Quality flags
Each redshift measurement is assigned a preliminary quality flag reproducing the flagging scheme presented in M17:
– Q=4: indicates a secure redshift measurement based on the identification of more than one emission line. Specifically, the Hαline is associated with the N
ii
doublet atλ6549.84 Å, λ6585.23 Å. In one case, the Oii
doublet (λ3727.09 Å and λ3729.88 Å) was identified rather than the Hαline. (Details on how the identification and fit of these groups of lines is performed is given in Sect.5.2);– Q=3.5: indicates a secure redshift measurement based on a single emission line (usually Hα);
– Q =3: indicates a likely secure redshift determination, but with a low probability of an incorrect identification or an uncertain redshift due to low signal-to-noise data or sky-line contamination affecting the Gaussian fit;
– Q =2: flag 2 indicates a reasonable but not secure enough guess. The targets being assigned with this flag are discarded from the calibration sample, and not included in the released catalogue.
5.2. Refine the redshift assignment withKUBEVIZ
Maps of the emission-line fluxes were obtained from the reduced data cube using the IDL routineKUBEVIZ(Fossati et al. 2016).
The code simultaneously fits groups of lines (defined as “line sets”, e.g. Hαand the N
ii
λ6548.05, λ6583.45 doublet, or the Oiii
λ4958.91, λ5006.84 doublet) using a combination of 1D Gaussian functions with fixed relative velocities. The continuum level is evaluated as the median value of the flux with an inten- sity from 40% to 60% within the total range of values inside two symmetric wavelength windows around each line set, and then subtracted. During the fit, KUBEVIZ takes into account the noise from the “stat” data cube, thus optimally suppress- ing sky-line residuals. Furthermore, we reject the spaxels with S NR<4.0 from the fit, and manually reject bad-fit and isolated spaxels from the map.There are several aspects that motivated us to useKUBEVIZ on the KMOS reduced data cubes. Firstly, fitting the Hα+N
ii
lineset improves the zspec measurement; starting from the Hα emission map of the galaxy and its corresponding velocity (v) map, we arbitrarily chose the centre (v = 0) of the galaxy as the spaxel that best compromises the peak of the Hαemission with the centre of the galaxy signal/velocity map (if present), and we corrected the inputzspecand the relative velocity of every spaxel accordingly. Furthermore, a successful KUBEVIZ fit of low-quality spectroscopic candidates (those that were assigned aQ=2 flag at the redshift assignment stage) allows their spec- troscopic confirmation by promoting the quality flag of thezspec
measurement, and thus their inclusion in the calibration sam- ple. Finally, theKUBEVIZoutputs constitute the groundwork for measuring the total Hαflux of the sources, which is described in detail in Sect.7.2.
5.3. Collecting multi-band photometry
We collected all available multi-wavelength photometry for the galaxy sample observed during the KMOS programme from public data releases in the three fields5.
5.3.1. COSMOS
We start from the COSMOS2015 catalogue released in Laigle et al.(2016), which contains precise PSF-matched pho- tometry for more than half a million sources in the COSMOS field. Among the wide collection of photometric bands avail- able in the data release, we selected CFHT u0 and Subaru B,V,R,i+,z+andz++ optical aperture magnitudes (300),Y JHK s near-infrared aperture magnitudes (300) from the UltraVISTA- DR2 survey, mid-infrared data from theSpitzerLarge Area Sur- vey with Hyper-Suprime-Cam (SPLASH) legacy programme
5 The multicolour photometry used here is optimised to measured physical parameters of galaxies of known spectroscopic redshift; other choices might be preferable when computing photometric redshifts (see Masters et al. 2015).
(IRAC ch1, ch2, ch3, ch4 total magnitudes), and GALEX NUV total magnitudes.
We computed total magnitudes in the optical and near- infrared domain starting from the aperture magnitudes and the correction factors given in the released catalogue using Eq. (9) in the appendix ofLaigle et al.(2016):
MAG_TOTALi,f =MAG_APER3i,f+oi−sf, (2) where i identifies the single objects, f the considered filter, MAG_APER3is the magnitudes computed within a 300radius aper- ture contained in the catalogue,oiis the photometric offset com- puted for scaling aperture magnitudes to total ones, andsf is the systematic offset computed in the paper using spectroscopic red- shifts. Finally, all magnitudes should also be corrected for fore- ground Galactic extinction using the reddening values given in the released catalogue for each object (Eq. (10) in the Appendix):
MAG_TOTALi,f,extcorr=MAG_TOTALi,f−E(B−V)i×Ff, (3) whereFf is the extinction factor of any given filter.
Besides the photometric information, we also kept thezphot
and physical properties (E(B−V), absolute magnitudes, median stellar masses, and SFR from the maximum likelihood – ML – analysis ofLePhare) derived inLaigle et al.(2016) by means of the SED fitting codeLePhare(Arnouts et al. 1999;Ilbert et al.
2006) run on the complete 30-band photometric data set.
5.3.2. SXDF
We collected multi-band photometry in the SPLASH survey data releaseMehta et al.(2018). We considered optical aperture mag- nitudes (300) from CFHTufilter and from the Hyper Suprime- Cam (HSC) UltraDeep layer in thegrizfilters; the near-infrared regime is fully covered by the VISTA Deep Extragalactic Obser- vations (VIDEO) SurveyY JHK saperture magnitudes (300), and the mid-infrared takes advantage of the IRAC coverage (ch1, ch2, ch3, ch4) from SPLASH.
Aperture magnitudes were corrected to total values using the offsets given in the released catalogue table (OFFSET_MAG) and all magnitudes were corrected for foreground extinction follow- ing the same procedure described in Sect.5.3.1for the COSMOS field. Consistent with what was done inLaigle et al.(2016) for the COSMOS field,Mehta et al.(2018) performed the SED fit- ting analysis of the SXDF photometric sample usingLePhare.
We took advantage of the outputs of their analysis to collect the physical properties of all our observed galaxies (E(B−V), abso- lute magnitudes, best fit stellar masses, and SFRs).
5.3.3. VVDS
A complete and homogeneous collection of photometry in the VVDS-02h field is contained in the VIDEO Survey, which has been merged with the CFHTLS Deep1 optical (ugriz) catalogue (M. Jarvis, & B. Häussler, priv. comm.). The catalogue con- tains aperture magnitudes within a 200 radius measured in a homogeneous manner in all the optical and near-infrared fil- ters. We computed the aperture to total magnitude offsets using the SExtractor MAG_AUTO values given in the catalogues and the photometric errors, according to Eqs. (4) and (5) in Laigle et al.(2016):
o= 1 P
filtersiwi
× X
filtersi
(MAGAUTO−MAGAPER)i×wi, (4)
Table 3.Success rate of KMOS observations.
Period H-band K-band
P99 72/88(?)(81.8%) 5/22(??)(22.7%) P100 106/176 (60.2%) 46/132 (34.8%) P101 53/89 (59.6%) 13/44 (29.5%) P102 117/220 (53.2%) 12/51 (30.4%) Total 348/573 (60.7%) 76/232 (32.8%)
Notes. (?)72 galaxies with accurate zspec estimate (Q ≥ 3) over 88 observed targets. (??)Pointing re-observed during P102. Since 17 out of 22 galaxies were re-observed, the contribution to the total number of observed objects in theK-band from P99 is just five.
where wi= 1
(σ2AUTO+σ2APER)i
· (5)
The offsets are computed for each object in the catalogue (i) using all the bandpasses in the optical and near-infrared domain.
We finally corrected total magnitudes for Milky Way foreground extinction using theSchlegel et al.(1998) maps (consistent with what was used inLaigle et al. 2016) at the coordinates of each object and using the appropriate filter factors, as given in Eq. (3).
In order to investigate and compare the properties of all the observed galaxies with the spectroscopically confirmed ones, and to have consistentzphotmeasurements throughout the three explored fields, we ranLePhareon the whole set of collected filters and derivedzphotand physical properties of all observed VVDS galaxies (E(B−V), absolute magnitudes, median stellar masses, and SFR from the ML analysis).
6. Results I: The success rate of the redshift assignment
In light of the concepts outlined above, the success rate (SR) of the KMOS spectroscopic campaign in the context of the C3R2 survey must be evaluated in two ways: (1) as any spec- troscopic survey, as the ratio (or, equivalently, percentage) of the total number of high-quality zspec measured with respect to the number of targets observed; (2) as the total number of empty/undersampled cells that are newly filled with spectroscop- ically confirmed galaxies. Needless to say, these two quantities should be considered together: a large number of high-quality zspecassigned to a small number of cells is less valuable than a smaller number of high-qualityzspeccovering a larger number of empty SOM cells.
The total number ofzphottargets observed with KMOS was 805, 424 of which provided a secure redshift measurement (Q≥ 3), leading to a total SR of 51.4%. The detailed SR of the four semesters and two filters is listed Table3. Overall, the SR of H-band observations is twice that of the K-band observations, likely primarily due to the higher backgrounds at longer wave- length. Additional challenges are caused by the lowering of the precision of currently available template fitting techniques as redshift increases, and also the lower brightness of the targets themselves. Doubling the exposure time ofK-band pointings and repeating the observation of twoK-band pointings observed dur- ing P99 and P100, was not conclusive in this respect: theK-band SR in P102 only slightly increased compared to previous peri- ods. Whether this result is mainly due to the limited accuracy ofzphot-based target selection or to the necessity of longer expo- sure times to increase the SNR of the spectra is still unclear, but
Fig. 2.Top left: comparison betweenzphotandzspecfor high-quality (Q≥3) redshift galaxies observed during the four periods of the KMOS Large Programme. Lower redshift targets are observed with theH-band grism, higher redshift ones with theK-band. The dashed lines define the region outside which thezphotis considered a “catastrophic failure” (grey area in the plot), defined by a redshift error|zphot−zspec|/(1+zspec)≥15%.Top right: histogram of the (zphot−zspec)/(1+zspec) of all high-quality redshift targets. A Gaussian with mean and sigma equal to the bias andσNMAD, respectively, is overplotted with a red dashed line.Bottom left: same as the top-left panel but comparingzphot,SOMandzspec.Bottom right: same as the top-right panel but withzphot,SOM.
a detailed analysis of the spectroscopic failures is presented in Sect.6.1.
Figure 2 presents a comparison between the photometric (individual and SOM-based) redshifts and high-quality (Q ≥ 3) KMOS spectroscopic redshifts. The dashed lines trace the boundaries outside which the photometric redshifts are consid- ered catastrophic outliers,|zphot−zspec|/(1+zspec) ≥ 15%. The top panels of Fig. 2 compare the individualzphot redshift esti- mates with our zspecmeasurements: according to these quanti- ties, our sample contains one catastrophic outlier. This galaxy, observed in the H-band, has a zphot = 1.6565, zspec = 1.2632 andQ = 3.0. A detailed analysis of this target revealed a dis- crepancy between the individual (from template fitting) and the SOM-basedzphotestimates (zphot,SOM=1.9407), which could be the reason of the misplacement of this target in the zspec–zphot
plane. Furthermore, we notice that there is a target observed in the H-band withzphot ≤ 1.6, but validated atzspec ≥ 2, thanks
to the identification of the O
iii
(λ4960.30 Å, 5008.24 Å) lines.The bottom panels of Fig.2show the same statistical analysis to compare thezspecwith the redshift of the SOM cell each galaxy belongs to (zphot,SOM).
We point out that the SOM is not intended to be used for individual redshift estimates, and therefore one should not be surprised that its performance in terms of recovering individual zphotvalues is worse than for individual multi-band template fit- ting. However, comparing the distribution of zphot andzspec in individual SOM cells is fundamental for a better understanding of cell occupation (e.g. in order to quantify thezphotdispersion of galaxies occupying the same cell or to pinpoint multiple peaks in the distribution of galaxies) and for highlighting problematic regions in the SOM.
The incidence of catastrophic outliers is significantly higher whenzphot,SOMis considered. These 25 galaxies fall into 18 dif- ferent cells in the SOM, and have an individualzphotmore in line
Fig. 3.Histogram ofzphotof galaxies populating each cell falling in the grey region of thezphot,SOM–zspecplane (bottom left panel of Fig.2). The distribution is normalised by dividing the number of galaxies in eachzphotbin by the total number ofzphotpopulating the considered cell; the number is indicated with the letterNin thetop left panelof the figures, and written at the same position in the others. Similarly, the cell number (Cell ID) and coordinates (Cell X, Cell Y) are also given inside each panel. Thezphot,SOMis represented by the dashed line, whereas dotted lines indicatezspecmeasured during our KMOS programme. The horizontal bar centred on the meanzphotis the rms of the histogram.
with the measuredzspec; furthermore, in case of multiple obser- vations within the same SOM cell, these galaxies have individual redshifts, which are in line with the other galaxies populating the cell. This result leads us to conclude that there is a misalignment between the redshift of the cell and the redshift of the individual galaxies that compose it. A better understanding of the distribu- tion of individualzphotof galaxies in the aforementioned SOM cells is given in Fig. 3. All galaxies in the C3R2 parent zphot
sample are used to populate the cells, and the zphot,SOM is also represented inside each panel with the dashed vertical line. As is noticeable from the dispersion values of the histograms (hor- izontal errorbars centred on the mean zphot), thezphot distribu- tion peaks close to thezphot,SOMvalue, but high dispersion and/or double peaks are present in many of the cells; multiple spectro- scopic redshift measurements occupy a narrow redshift range in the panels, often separated from the zphot,SOM.Euclid galaxies that are assigned to these problematic cells need to be flagged, as their photometric redshift could be difficult to calibrate.
The mean value of the redshift difference Mean zphot−zspec
1+zspec
!
(6) is represented as the mean value of the (red dashed) Gaussian in Fig.2. When comparingzspecwith the individualzphot, the value is −0.0029, and −0.0070 and 0.0148 separately in the H- and K-bands, respectively, further confirming the decreasing preci- sion of current photometric redshift estimates with increasing redshift. The redshift difference increases to 0.027 when con- sidering the comparison betweenzspecandzphot,SOM, and 0.030, 0.013 in the H- and K-bands, respectively. The higherH-band bias reflects the increased number of catastrophic outliers, which are all located atzspec≤1.75.
The normalised median absolute deviation, a dispersion mea- sure that is not sensitive to catastrophic outliers (Ilbert et al.
2009;Dahlen et al. 2013), defined as σNMAD=1.48×median |zphot−zspec|
1+zspec
!
, (7)
is 0.0301 (3%) when individualzphotare considered, and 0.0443 (&4%) when zphot,SOM are used, pointing out that not only the number of catastrophic outliers increases, but also the dispersion of the data points in the white region of the (left-hand panels) scatter plots in Fig.2. The values of the∆hziandσNMADare in agreement with the results presented in M17 and M19.
We computed the number of cells containing P1/P2 targets (according to the priorities defined in Sect. 3.2) with a SOM photometric redshift 1.3 <zphot,SOM < 1.7 (forH-band targets) and 2.0 < zphot,SOM < 2.5 (forK-band targets). The SOM has a number of P1 and P2 cells in this redshift range of 283 and 327, respectively. These numbers indicate the nominal goal of C3R2 in the near-infrared, and will be used as a reference. The number of P1/P2 cells covered by all KMOS observations (i.e.
by all targets placed in KMOS pointings from P99 until P103) is 274 and 162, respectively. Of the P1 cells occupied by the KMOSzphotcandidates, 57% (156/274) were spectroscopically confirmed, and the percentage increases to 70% (113/162) for the P2 targets. The result is represented in Fig.4. The histograms shown in Fig.4clearly mirror our observing strategy; we prefer- entially observed P1 targets covering empty SOM cells, and used P2 targets as fillers for optimising and maximising the number of observed galaxies in one pointing.
6.1. Spectroscopic failures and uncalibrated cells
We next analysed the properties of galaxies that were observed but for which we could not assign a spectroscopic redshift. The main purpose of this analysis is to understand whether there are biases in the data and where these failures are located in
Fig. 4.Success rate in terms of number of cells filled with high-quality zspec. The observed targets are divided into high (P1) and low (P2) prior- ity targets according to the prioritisation scheme described in Sect.3.2.
Purple horizontal bars represent the total number of undersampled cells requiringzspecmeasurements; orange histograms represent the number of cells targeted by all KMOS observations, and green histograms rep- resent the number of cells that provided accuratezspecmeasurements.
the SOM. To this end, we considered the physical parameters derived from SED fittings inLaigle et al.(2016) andMehta et al.
(2018) for the COSMOS and the SXDF field, respectively. The reason for this choice is twofold. First, when trying to explore the properties of non-spectroscopically validated galaxies, we are forced to rely onzphot- andzphot-based physical parameters, which are better determined when a broader photometric sam- ple in terms of the number of available filters is used. Both Laigle et al.(2016) andMehta et al.(2018) based their SED fit- ting analyses on a broad number of filters spanning the whole spectrum. Furthermore, the two are comparable as the same PSF homogeneisation was adopted for the data, and the same tem- plate library was used for photometric redshift calculation. Sec- ondly, our LePhare setup is a close imitation of what was per- formed in the two data releases, though limited to a restricted number of filters. In order to check that we did not introduce any bias, we ran LePhare on the photometric samples with the same configuration described in Sect.7, but without fixing the redshift, and we compared the results with those fromLaigle et al.(2016) andMehta et al.(2018). In the COSMOS field, the average dif- ference between stellar masses is 0.090 with an rms of 0.17, and between the (SED fitting based) SFRs it is 0.003 with an rms of 0.229. In the SXDF field, the average difference between stellar masses is 0.069 with a rms of 0.313 and between the (SED fitting based) SFR is 0.237 with a rms of 0.473. In light of the above, our set of physical parameters is compatible within the errors with the literature but with larger uncertainties. Although all the conclusions discussed below do not change with our derivation, in the following we always refer to the results from the literature.
Figure 5 illustrates the distributions of the zphot, observed totalHmagnitudes and SED-fitting star formation rates (SFRs), and stellar masses for all galaxies observed during our KMOS programme (green histograms), for the sub-samples of spectro- scopically confirmed targets (orange histograms) and for the tar-
gets that could not be assigned a redshift (blue open histograms).
The distributions of validated and non-validated targets present some differences, with the former being slightly brighter with a higher star formation rate: the median value ofHis 22.78 in the validated sample and 22.84 in the non-validated one. Simi- larly, the median log10(SFR/Myr−1) values are 1.41 and 1.21 in the two samples, respectively. From the bottom right panel of the figure, we can finally notice that our spectroscopic com- pleteness, in terms of number of galaxies validated with respect to the total number of galaxies observed, is a function of stellar mass. Specifically, at low stellar masses (log10(M?/M)<9.5), the fraction of validated targets is around 0.5, likely reflecting the low SNR deriving from the limited integration time of our obser- vations; the ratio between validated targets and observed ones reaches the value of 0.7 at 9.5<log10(M?/M)<10 and finally decreases to the lowest values at higher stellar masses. A better understanding of the reasons that prevented us from assigning a high-quality spectroscopic redshift to all galaxies can be reached by analysing the distribution of the validated and non-validated targets in the SOM.
In the central panel of Fig.6, validated cells are colour-coded according to the value of the assignedzspec. Cells populated with multiple observations have been assigned a medianzspecvalue.
This panel again highlights a prevalence of low-redshift targets as already discussed in Sect.6, mainly concentrated at low val- ues of theX-indices, and spread along the wholeY-index range.
In the right panel, we show thezphotof the observed targets for which we could not measurezspec, and we mask the spectroscop- ically confirmed cells. The comparison between the zspec and zphot SOMs confirms that, despite the higher number of spec- troscopically confirmed H-band targets, there is no systematic (photometric) redshift bias in the observed and non-validated tar- gets: the SOM cells that were observed but could not be filled with a highly confidentzspechave values ranging from the lowest H-band to the highest redshifts reachable with theK-band setup.
However, if the lack of measurement is due to observational dif- ficulties in theK-band and lower accuracy in the SED fittingzphot
determination used to select the observed targets, the cause of the concentration of lower redshift (H-band) galaxies present in the bottom region of the SOM (dark blue cells) must be investigated more thoroughly.
We searched for the reason behind these spectroscopic fail- ures in the colours and star formation properties of galaxies.
Figure 7 represents the rest-frame (u −g) colour, the best fit E(B−V), the and SED fitting SFR of the non-validated sam- ple. Again, the cells containing more than one target have been assigned a median value. The peculiarity of the bottom part of the SOM stands out: the galaxies populating these cells are, on average, redder and have lower star formation rates compared to the other empty cells. Moreover, as it noticeable from the E(B−V) shown in the middle panel, they are not particularly dusty. Our observing strategy, and in particular the integration time, may require modifications for obtaining the necessary SNR required to measure emission-line redshifts.
7. Results II: The physical properties of galaxies 7.1. SED fitting analysis
The physical properties of galaxies were derived again for the spectroscopically confirmed targets, by taking advantage of the use of zspec as a constraint to the fit. We applied the SED fit- ting codeLePhareto the spectrophometric catalogues obtained from merging the spectroscopic redshift measurement with the
Fig. 5.Top left: histogram ofzphotof individual galaxies from the literature.Top right: histogram of the observedHtotal magnitude for all observed targets (green filled), for those with high-quality spectroscopic redshifts (validated targets; orange filled) and for those that could not be assigned a spectroscopic redshift (not validated targets; open blue line).Bottom left: histogram of the SFR derived from SED fitting for the same samples.
Bottom right: histogram of the stellar mass derived from SED fitting for the same samples.
multi-band photometry collected from the parent surveys. A detailed list of the filters used in the three fields is reported in Table4, and the appropriate reference to the parent photomet- ric catalogues is given in the table caption. The code is provided with spectroscopic redshifts and total magnitudes as input, and we set the priors on fitting parameters and galaxy libraries (based on a collection of different star formation histories, SFHs) taking advantage of the knowledge of the average properties of our tar- get galaxies: these are high-redshift, star-forming galaxies, with consistent Hαemission. Out of the whole library of available models, we selected a number of exponentially declining SFHs (τmodels), of delayed SFH and of constant SFR, with sub-solar (Z = 0.008) and solar (Z = Z = 0.02) metallicity. We used a fine grid of E(B−V) ranging from 0 to 0.7, and two differ- ent extinction laws (Calzetti et al. 2000;Arnouts et al. 2013), are also adopted. We obtain the stellar masses, absolute magnitudes, best fitE(B−V) values, and other physical parameters such as
the SFR as output. In the following, for stellar masses and SED fitting SFRs, we use the median values computed from the ML analysis ofLePhare.
The histogram of the resulting stellar masses fromLePhare in the three fields is shown in Fig. 8. The median stellar mass value in the total spectrophotometric sample of galaxies observed during the KMOS programme is log10(M?/M) = 9.69, and the values in the three different fields are:
log10(M?/M)COSMOS = 9.73, log10(M?/M)SXDF = 9.84, log10(M?/M)VVDS=9.62.
Besides the primary goal of determining and calibrating P(z|C), the properties of the galaxies observed by the C3R2 sur- vey is of unique importance and interest. Building a sample of spectra spanning the whole redshift range up to z ∼ 2.5 and covering the whole galaxy colour space will shed light on con- troversial aspects of galaxy evolution studies and will help the acquisition of a general and complete picture of the galaxy
